import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# check for GPU
device = 0 if torch.cuda.is_available() else -1


format_input = (
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n{instruction}\n\n### Response:"
)


class EndpointHandler:
    def __init__(self, path=""):
        # load the model
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoModelForCausalLM.from_pretrained(
            path,
            device_map="auto",
            torch_dtype=torch.float16,
        )
        # create inference pipeline
        self.pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            device=device,
            max_length=256,
        )

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        text_input = format_input.format(instruction=inputs)

        # pass inputs with all kwargs in data
        if parameters is not None:
            prediction = self.pipeline(text_input, **parameters)
        else:
            prediction = self.pipeline(text_input)

        # postprocess the prediction
        output = [
            {"generated_text": pred["generated_text"].split("### Response:")[1].strip()}
            for pred in prediction
        ]

        return output
